Segmentation of ultrasound image sequences by combing a novel deep siamese network with a deformable contour model

Deformable contours are widely applied in medical image segmentation, which are usually derived from appearance cues in medical images. However, the performance of deformed contour is suppressed in ultrasonic image segmentation by the weak, misleading boundaries and the complex shapes of lesion regions. In this paper, a novel deformable contour model is proposed for segmenting ultrasound image sequences, which aims to utilize the powerful ability of deep learning network in learning of image features to help the deformable contour model resist weaknessses of ultrasound images. The deep learning network is designed as a densely connected siamese architecture. It trains a contrastive loss that serves as a boundary searching metric of a deformable contour to segment ultrasound image sequences. In this network, the densely residual blocks and the attention focused blocks are designed to make the network efficiently propagate features and focus on the lesion region, and the feature memory module stores and generates the prior features to aid the evolution of a deformable contour. Moreover, for resisting the impact of misleading or weak boundary, the shape similarity of lesion regions is used to as a shape prior and integrated into the framework of deformable contour to constrain the change of contours. The experimental results for the clinical ultrasound image sequences demonstrate that compared to the state-of-the-art methods, the proposed method can provide more accurate results in HIFU ultrasound images.